"""simple docstring"""



import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         MODEL_FOR_PRETRAINING_MAPPING,
         NezhaForMaskedLM,
         NezhaForMultipleChoice,
         NezhaForNextSentencePrediction,
         NezhaForPreTraining,
         NezhaForQuestionAnswering,
         NezhaForSequenceClassification,
         NezhaForTokenClassification,
         NezhaModel,
     )
     from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST

class SCREAMING_SNAKE_CASE  :







 """simple docstring"""


 def __init__( self		:   Dict       , _snake_case		:   Optional[Any]       , _snake_case		:   str=13       , _snake_case		:   Union[str, Any]=7       , _snake_case		:   Any=True       , _snake_case		:   Any=True       , _snake_case		:   str=True       , _snake_case		:   Optional[int]=True       , _snake_case		:   Optional[int]=99       , _snake_case		:   Union[str, Any]=32       , _snake_case		:   Optional[int]=5       , _snake_case		:   str=4       , _snake_case		:   int=37       , _snake_case		:   Dict="gelu"       , _snake_case		:   str=0.1       , _snake_case		:   Any=0.1       , _snake_case		:   List[str]=128       , _snake_case		:   int=32       , _snake_case		:   List[str]=16       , _snake_case		:   Optional[int]=2       , _snake_case		:   str=0.02       , _snake_case		:   List[str]=3       , _snake_case		:   Any=4       , _snake_case		:   str=None       , )					->      Tuple:




    '''simple docstring'''






    a__  					=		parent
    a__  					=		batch_size
    a__  					=		seq_length
    a__  					=		is_training
    a__  					=		use_input_mask
    a__  					=		use_token_type_ids
    a__  					=		use_labels
    a__  					=		vocab_size
    a__  					=		hidden_size
    a__  					=		num_hidden_layers
    a__  					=		num_attention_heads
    a__  					=		intermediate_size
    a__  					=		hidden_act
    a__  					=		hidden_dropout_prob
    a__  					=		attention_probs_dropout_prob
    a__  					=		max_position_embeddings
    a__  					=		type_vocab_size
    a__  					=		type_sequence_label_size
    a__  					=		initializer_range
    a__  					=		num_labels
    a__  					=		num_choices
    a__  					=		scope


 def 							_lowerCAmelCase					( self		:   str					)					->      Tuple:




    '''simple docstring'''






    a__  					=		ids_tensor([self.batch_size, self.seq_length]       , self.vocab_size					)

    a__  					=		None
    if self.use_input_mask:
       a__  					=		random_attention_mask([self.batch_size, self.seq_length]					)

    a__  					=		None
    if self.use_token_type_ids:
       a__  					=		ids_tensor([self.batch_size, self.seq_length]       , self.type_vocab_size					)

    a__  					=		None
    a__  					=		None
    a__  					=		None
    if self.use_labels:
       a__  					=		ids_tensor([self.batch_size]       , self.type_sequence_label_size					)
       a__  					=		ids_tensor([self.batch_size, self.seq_length]       , self.num_labels					)
       a__  					=		ids_tensor([self.batch_size]       , self.num_choices					)

    a__  					=		self.get_config()

    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


 def 							_lowerCAmelCase					( self		:   Union[str, Any]					)					->      Any:




    '''simple docstring'''






    return NezhaConfig(
        vocab_size=self.vocab_size       , hidden_size=self.hidden_size       , num_hidden_layers=self.num_hidden_layers       , num_attention_heads=self.num_attention_heads       , intermediate_size=self.intermediate_size       , hidden_act=self.hidden_act       , hidden_dropout_prob=self.hidden_dropout_prob       , attention_probs_dropout_prob=self.attention_probs_dropout_prob       , max_position_embeddings=self.max_position_embeddings       , type_vocab_size=self.type_vocab_size       , is_decoder=_UpperCAmelCase       , initializer_range=self.initializer_range       , )


 def 							_lowerCAmelCase					( self		:   Any					)					->      Tuple:




    '''simple docstring'''






    (
        (
        a__	
    )					,							(
        a__	
    )					,							(
        a__	
    )					,							(
        a__	
    )					,							(
        a__	
    )					,							(
        a__	
    )					,							(
        a__	
    )					,								
    )  					=		self.prepare_config_and_inputs()

    a__  					=		True
    a__  					=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
    a__  					=		ids_tensor([self.batch_size, self.seq_length]       , vocab_size=2					)

    return (
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    )


 def 							_lowerCAmelCase					( self		:   Union[str, Any]       , _snake_case		:   Optional[int]       , _snake_case		:   Optional[Any]       , _snake_case		:   Union[str, Any]       , _snake_case		:   Dict       , _snake_case		:   Any       , _snake_case		:   Union[str, Any]       , _snake_case		:   List[Any]					)					->      Any:




    '''simple docstring'''






    a__  					=		NezhaModel(config=_UpperCAmelCase					)
    model.to(_UpperCAmelCase					)
    model.eval()
    a__  					=		model(_UpperCAmelCase       , attention_mask=_UpperCAmelCase       , token_type_ids=_UpperCAmelCase					)
    a__  					=		model(_UpperCAmelCase       , token_type_ids=_UpperCAmelCase					)
    a__  					=		model(_UpperCAmelCase					)
    self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size)					)
    self.parent.assertEqual(result.pooler_output.shape       , (self.batch_size, self.hidden_size)					)


 def 							_lowerCAmelCase					( self		:   Union[str, Any]       , _snake_case		:   List[str]       , _snake_case		:   Dict       , _snake_case		:   int       , _snake_case		:   Tuple       , _snake_case		:   List[str]       , _snake_case		:   List[str]       , _snake_case		:   Union[str, Any]       , _snake_case		:   Optional[int]       , _snake_case		:   List[str]       , )					->      Dict:




    '''simple docstring'''






    a__  					=		True
    a__  					=		NezhaModel(_UpperCAmelCase					)
    model.to(_UpperCAmelCase					)
    model.eval()
    a__  					=		model(
        _UpperCAmelCase       , attention_mask=_UpperCAmelCase       , token_type_ids=_UpperCAmelCase       , encoder_hidden_states=_UpperCAmelCase       , encoder_attention_mask=_UpperCAmelCase       , )
    a__  					=		model(
        _UpperCAmelCase       , attention_mask=_UpperCAmelCase       , token_type_ids=_UpperCAmelCase       , encoder_hidden_states=_UpperCAmelCase       , )
    a__  					=		model(_UpperCAmelCase       , attention_mask=_UpperCAmelCase       , token_type_ids=_UpperCAmelCase					)
    self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size)					)
    self.parent.assertEqual(result.pooler_output.shape       , (self.batch_size, self.hidden_size)					)


 def 							_lowerCAmelCase					( self		:   List[str]       , _snake_case		:   Union[str, Any]       , _snake_case		:   Optional[Any]       , _snake_case		:   Optional[Any]       , _snake_case		:   Union[str, Any]       , _snake_case		:   int       , _snake_case		:   Any       , _snake_case		:   int					)					->      Tuple:




    '''simple docstring'''






    a__  					=		NezhaForMaskedLM(config=_UpperCAmelCase					)
    model.to(_UpperCAmelCase					)
    model.eval()
    a__  					=		model(_UpperCAmelCase       , attention_mask=_UpperCAmelCase       , token_type_ids=_UpperCAmelCase       , labels=_UpperCAmelCase					)
    self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.vocab_size)					)


 def 							_lowerCAmelCase					( self		:   List[str]       , _snake_case		:   Optional[int]       , _snake_case		:   Union[str, Any]       , _snake_case		:   Any       , _snake_case		:   int       , _snake_case		:   List[str]       , _snake_case		:   Optional[Any]       , _snake_case		:   List[str]					)					->      List[str]:




    '''simple docstring'''






    a__  					=		NezhaForNextSentencePrediction(config=_UpperCAmelCase					)
    model.to(_UpperCAmelCase					)
    model.eval()
    a__  					=		model(
        _UpperCAmelCase       , attention_mask=_UpperCAmelCase       , token_type_ids=_UpperCAmelCase       , labels=_UpperCAmelCase       , )
    self.parent.assertEqual(result.logits.shape       , (self.batch_size, 2)					)


 def 							_lowerCAmelCase					( self		:   List[Any]       , _snake_case		:   int       , _snake_case		:   str       , _snake_case		:   str       , _snake_case		:   List[Any]       , _snake_case		:   str       , _snake_case		:   Optional[int]       , _snake_case		:   int					)					->      Dict:




    '''simple docstring'''






    a__  					=		NezhaForPreTraining(config=_UpperCAmelCase					)
    model.to(_UpperCAmelCase					)
    model.eval()
    a__  					=		model(
        _UpperCAmelCase       , attention_mask=_UpperCAmelCase       , token_type_ids=_UpperCAmelCase       , labels=_UpperCAmelCase       , next_sentence_label=_UpperCAmelCase       , )
    self.parent.assertEqual(result.prediction_logits.shape       , (self.batch_size, self.seq_length, self.vocab_size)					)
    self.parent.assertEqual(result.seq_relationship_logits.shape       , (self.batch_size, 2)					)


 def 							_lowerCAmelCase					( self		:   Union[str, Any]       , _snake_case		:   int       , _snake_case		:   Union[str, Any]       , _snake_case		:   Any       , _snake_case		:   Optional[int]       , _snake_case		:   Optional[Any]       , _snake_case		:   Optional[int]       , _snake_case		:   str					)					->      Union[str, Any]:




    '''simple docstring'''






    a__  					=		NezhaForQuestionAnswering(config=_UpperCAmelCase					)
    model.to(_UpperCAmelCase					)
    model.eval()
    a__  					=		model(
        _UpperCAmelCase       , attention_mask=_UpperCAmelCase       , token_type_ids=_UpperCAmelCase       , start_positions=_UpperCAmelCase       , end_positions=_UpperCAmelCase       , )
    self.parent.assertEqual(result.start_logits.shape       , (self.batch_size, self.seq_length)					)
    self.parent.assertEqual(result.end_logits.shape       , (self.batch_size, self.seq_length)					)


 def 							_lowerCAmelCase					( self		:   Optional[Any]       , _snake_case		:   str       , _snake_case		:   Tuple       , _snake_case		:   Optional[int]       , _snake_case		:   Dict       , _snake_case		:   Dict       , _snake_case		:   Tuple       , _snake_case		:   List[str]					)					->      Dict:




    '''simple docstring'''






    a__  					=		self.num_labels
    a__  					=		NezhaForSequenceClassification(_UpperCAmelCase					)
    model.to(_UpperCAmelCase					)
    model.eval()
    a__  					=		model(_UpperCAmelCase       , attention_mask=_UpperCAmelCase       , token_type_ids=_UpperCAmelCase       , labels=_UpperCAmelCase					)
    self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_labels)					)


 def 							_lowerCAmelCase					( self		:   List[Any]       , _snake_case		:   Optional[Any]       , _snake_case		:   List[str]       , _snake_case		:   str       , _snake_case		:   List[Any]       , _snake_case		:   Optional[Any]       , _snake_case		:   str       , _snake_case		:   List[Any]					)					->      Union[str, Any]:




    '''simple docstring'''






    a__  					=		self.num_labels
    a__  					=		NezhaForTokenClassification(config=_UpperCAmelCase					)
    model.to(_UpperCAmelCase					)
    model.eval()
    a__  					=		model(_UpperCAmelCase       , attention_mask=_UpperCAmelCase       , token_type_ids=_UpperCAmelCase       , labels=_UpperCAmelCase					)
    self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.num_labels)					)


 def 							_lowerCAmelCase					( self		:   List[Any]       , _snake_case		:   Union[str, Any]       , _snake_case		:   str       , _snake_case		:   int       , _snake_case		:   Dict       , _snake_case		:   List[str]       , _snake_case		:   int       , _snake_case		:   Dict					)					->      Optional[int]:




    '''simple docstring'''






    a__  					=		self.num_choices
    a__  					=		NezhaForMultipleChoice(config=_UpperCAmelCase					)
    model.to(_UpperCAmelCase					)
    model.eval()
    a__  					=		input_ids.unsqueeze(1					).expand(-1       , self.num_choices       , -1					).contiguous()
    a__  					=		token_type_ids.unsqueeze(1					).expand(-1       , self.num_choices       , -1					).contiguous()
    a__  					=		input_mask.unsqueeze(1					).expand(-1       , self.num_choices       , -1					).contiguous()
    a__  					=		model(
        _UpperCAmelCase       , attention_mask=_UpperCAmelCase       , token_type_ids=_UpperCAmelCase       , labels=_UpperCAmelCase       , )
    self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_choices)					)



 def 							_lowerCAmelCase					( self		:   Optional[Any]					)					->      Optional[Any]:




    '''simple docstring'''






    a__  					=		self.prepare_config_and_inputs()
    (
        (
        a__	
    )					,							(
        a__	
    )					,							(
        a__	
    )					,							(
        a__	
    )					,							(
        a__	
    )					,							(
        a__	
    )					,							(
        a__	
    )					,								
    )  					=		config_and_inputs
    a__  					=		{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
    return config, inputs_dict

@require_torch
class SCREAMING_SNAKE_CASE  (     snake_case__ ,   snake_case__ ,   snake_case__ ,   unittest.TestCase						):







 """simple docstring"""


 a_							:						Union[str, Any]	      =(
     (
         NezhaModel,
         NezhaForMaskedLM,
         NezhaForMultipleChoice,
         NezhaForNextSentencePrediction,
         NezhaForPreTraining,
         NezhaForQuestionAnswering,
         NezhaForSequenceClassification,
         NezhaForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 a_							:						Optional[Any]	      =(
     {
         '''feature-extraction''': NezhaModel,
         '''fill-mask''': NezhaForMaskedLM,
         '''question-answering''': NezhaForQuestionAnswering,
         '''text-classification''': NezhaForSequenceClassification,
         '''token-classification''': NezhaForTokenClassification,
         '''zero-shot''': NezhaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 a_							:						Tuple	      =True


 def 							_lowerCAmelCase					( self		:   Dict       , _snake_case		:   Any       , _snake_case		:   Optional[int]       , _snake_case		:   str=False					)					->      Tuple:




    '''simple docstring'''






    a__  					=		super()._prepare_for_class(_UpperCAmelCase       , _UpperCAmelCase       , return_labels=_UpperCAmelCase					)

    if return_labels:
       if model_class in get_values(_UpperCAmelCase					):
          a__  					=		torch.zeros(
              (self.model_tester.batch_size, self.model_tester.seq_length)       , dtype=torch.long       , device=_UpperCAmelCase					)
          a__  					=		torch.zeros(
              self.model_tester.batch_size       , dtype=torch.long       , device=_UpperCAmelCase					)
    return inputs_dict


 def 							_lowerCAmelCase					( self		:   Optional[int]					)					->      str:




    '''simple docstring'''






    a__  					=		NezhaModelTester(self					)
    a__  					=		ConfigTester(self       , config_class=_UpperCAmelCase       , hidden_size=37					)


 def 							_lowerCAmelCase					( self		:   Dict					)					->      Union[str, Any]:




    '''simple docstring'''






    self.config_tester.run_common_tests()


 def 							_lowerCAmelCase					( self		:   str					)					->      Dict:




    '''simple docstring'''






    a__  					=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_UpperCAmelCase					)


 def 							_lowerCAmelCase					( self		:   int					)					->      str:




    '''simple docstring'''






    a__  					=		self.model_tester.prepare_config_and_inputs_for_decoder()
    self.model_tester.create_and_check_model_as_decoder(*_UpperCAmelCase					)


 def 							_lowerCAmelCase					( self		:   Union[str, Any]					)					->      int:




    '''simple docstring'''






    (
        (
        a__	
    )					,							(
        a__	
    )					,							(
        a__	
    )					,							(
        a__	
    )					,							(
        a__	
    )					,							(
        a__	
    )					,							(
        a__	
    )					,							(
        a__	
    )					,							(
        a__	
    )					,								
    )  					=		self.model_tester.prepare_config_and_inputs_for_decoder()

    a__  					=		None

    self.model_tester.create_and_check_model_as_decoder(
        _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , )


 def 							_lowerCAmelCase					( self		:   List[str]					)					->      List[Any]:




    '''simple docstring'''






    a__  					=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase					)


 def 							_lowerCAmelCase					( self		:   Union[str, Any]					)					->      Optional[Any]:




    '''simple docstring'''






    a__  					=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase					)


 def 							_lowerCAmelCase					( self		:   Union[str, Any]					)					->      Optional[int]:




    '''simple docstring'''






    a__  					=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_next_sequence_prediction(*_UpperCAmelCase					)


 def 							_lowerCAmelCase					( self		:   Union[str, Any]					)					->      int:




    '''simple docstring'''






    a__  					=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_pretraining(*_UpperCAmelCase					)


 def 							_lowerCAmelCase					( self		:   Dict					)					->      Any:




    '''simple docstring'''






    a__  					=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase					)


 def 							_lowerCAmelCase					( self		:   Dict					)					->      List[str]:




    '''simple docstring'''






    a__  					=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase					)


 def 							_lowerCAmelCase					( self		:   Optional[int]					)					->      Dict:




    '''simple docstring'''






    a__  					=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase					)


 @slow
 def 							_lowerCAmelCase					( self		:   int					)					->      Optional[int]:




    '''simple docstring'''






    for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       a__  					=		NezhaModel.from_pretrained(_UpperCAmelCase					)
       self.assertIsNotNone(_UpperCAmelCase					)



 @slow
 @require_torch_gpu
 def 							_lowerCAmelCase					( self		:   Optional[Any]					)					->      Union[str, Any]:




    '''simple docstring'''






    a__					,							a__  					=		self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes:
       # NezhaForMultipleChoice behaves incorrectly in JIT environments.
       if model_class == NezhaForMultipleChoice:
          return

       a__  					=		True
       a__  					=		model_class(config=_UpperCAmelCase					)

       a__  					=		self._prepare_for_class(_UpperCAmelCase       , _UpperCAmelCase					)
       a__  					=		torch.jit.trace(
           _UpperCAmelCase       , (inputs_dict['input_ids'].to('cpu'					), inputs_dict['attention_mask'].to('cpu'					))					)

       with tempfile.TemporaryDirectory() as tmp:
          torch.jit.save(_UpperCAmelCase       , os.path.join(_UpperCAmelCase       , 'bert.pt'					)					)
          a__  					=		torch.jit.load(os.path.join(_UpperCAmelCase       , 'bert.pt'					)       , map_location=_UpperCAmelCase					)
          loaded(inputs_dict['input_ids'].to(_UpperCAmelCase					)       , inputs_dict['attention_mask'].to(_UpperCAmelCase					)					)

@require_torch
class SCREAMING_SNAKE_CASE  (     unittest.TestCase						):







 """simple docstring"""


 @slow
 def 							_lowerCAmelCase					( self		:   Optional[int]					)					->      Union[str, Any]:




    '''simple docstring'''






    a__  					=		NezhaModel.from_pretrained('sijunhe/nezha-cn-base'					)
    a__  					=		torch.tensor([[0, 1, 2, 3, 4, 5]]					)
    a__  					=		torch.tensor([[0, 1, 1, 1, 1, 1]]					)
    with torch.no_grad():
       a__  					=		model(_UpperCAmelCase       , attention_mask=_UpperCAmelCase					)[0]
    a__  					=		torch.Size((1, 6, 768)					)
    self.assertEqual(output.shape       , _UpperCAmelCase					)
    a__  					=		torch.tensor([[[0.0685, 0.2441, 0.1102], [0.0600, 0.1906, 0.1349], [0.0221, 0.0819, 0.0586]]]					)

    self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       , _UpperCAmelCase       , atol=1E-4					)					)



 @slow
 def 							_lowerCAmelCase					( self		:   Any					)					->      List[Any]:




    '''simple docstring'''






    a__  					=		NezhaForMaskedLM.from_pretrained('sijunhe/nezha-cn-base'					)
    a__  					=		torch.tensor([[0, 1, 2, 3, 4, 5]]					)
    a__  					=		torch.tensor([[1, 1, 1, 1, 1, 1]]					)
    with torch.no_grad():
       a__  					=		model(_UpperCAmelCase       , attention_mask=_UpperCAmelCase					)[0]
    a__  					=		torch.Size((1, 6, 2_1128)					)
    self.assertEqual(output.shape       , _UpperCAmelCase					)
    a__  					=		torch.tensor(
        [[-2.7939, -1.7902, -2.2189], [-2.8585, -1.8908, -2.3723], [-2.6499, -1.7750, -2.2558]]					)

    self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       , _UpperCAmelCase       , atol=1E-4					)					)







'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  logging.get_logger(__name__)

class 							a__( snake_case__     ):






			def __init__(     self					,   *_UpperCAmelCase					,   **_UpperCAmelCase						)							->							None:
					warnings.warn(
					    'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
					    ' use GLPNImageProcessor instead.'					,   _UpperCAmelCase					,   )
					super().__init__(*_UpperCAmelCase					,   **_UpperCAmelCase						)




